Different Aspects for Enhancing The Backpropagation Neural Networks

Document Type : Original Article

Abstract

Backpropagation (BP) algorithm is one of the most popular training algorithms for
multilayer neural networks. The convergence of backpropagation learning is
analyzed so as to explain common phenomenon observed by specialists. The
performance of the backpropagation algorithm is studied, analysed and evaluated in
this paper. A method for accelerating the convergence rate is presented. It provides
useful guidelines for thinking about how to accelerate the convergence through
learning rate adaptation. This work has been implemented through computer
simulated using C# with different activation functions and different methods for
representing the learning rates. The obtained results are encourage and promising.

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